This invention is in the field of oil and gas (“hydrocarbon”) production. Embodiments of this invention are more specifically directed to systems and methods for modeling and simulating the behavior of hydrocarbon reservoirs.
In the current economic climate, the optimization of oil and gas production from identified reservoirs has become especially important. In this regard, considering that much of the readily available oil and gas reservoirs have been exploited or are currently in production, production of oil and gas in less producible forms, or from formations that are more reluctant to release their hydrocarbons, have become of increased interest. For example, large reservoirs of natural gas yet remain in so-called “tight” formations, in which the flow of gas into a production well is greatly restricted by the nature of the gas-bearing rock. These low permeability formations include tight sands, gas shales and gas coals, requiring such actions as hydraulic fracturing (“fracing”) to raise production levels. In the oil context, production of heavy oil from unconsolidated sands (“UCS”) has become economically attractive, even from cold climates such as northern North America. Especially in difficult formations such as these, the high economic stakes require operators to devote substantial resources toward effective management of oil and gas reservoirs and individual wells within production fields.
Recent advances in computational capability, in combination with the high economic stakes involved in reservoir and well management, have motivated reservoir engineers to develop models of reservoir behavior, for example based on seismic and other geological surveys of the production field, along with conclusions that can be drawn from well logs, pressure transient analysis, and the like. These models are applied to conventional reservoir “simulator” computer programs, by way of which the reservoir engineer can analyze the behavior of the reservoir over its production history, and by way of which the engineer can simulate the behavior of the reservoir in response to potential reservoir management actions (i.e., “what-if” analysis). An example of such a reservoir management action is the injection of gas or water into the reservoir to provide additional “drive” as reservoir pressure drops over cumulative production. Modern reservoir simulation systems and software packages assist the operator in deciding whether to initiate or cease such “waterflood” operations, how many wells are to serve as injection wells, the locations of those injectors in the field, and the like.
Some reservoir simulators approximate fluid flow in the reservoir on a grid of geometric elements, and numerically simulate fluid flow behavior using finite-difference or finite-element techniques to solve for pressure and flow conditions within and between elements in the grid. In such simulation, the state of the reservoir model is stepped in time from some defined initial conditions, allowing the simulation package to evaluate inter-element flows, pressures at each grid element, and the like, at each point within a sequence of time steps. The results of this simulation can, if reasonably accurate, provide the reservoir engineer with insight into the expected behavior of the reservoir over time.
Because the geographical scale of typical reservoir models is relatively large, extending over hundreds of yards or several miles, corresponding finite-difference production field models of even modest complexity can become quite large, in the number of grid cells or mesh nodes. The computational complexity and cost of simulating the behavior of models including large numbers of cells or nodes can thus become prohibitive, even with modern high performance computer systems. As such, it is useful to reduce the number of grid cells in the model, by increasing the volume of each grid cell. For example, a typical grid cell in a reasonably manageable finite-difference model of a large production field may be on the order of hundreds of feet on a side. And because the time frame over which the simulation is carried out is often relatively long (e.g., from weeks to years), the time steps between solution points can be relatively long (e.g., once daily to monthly) to keep the computational burden somewhat reasonable.
However, it has been observed, in connection with this invention, that some physical phenomena in some of these newly-exploited formations cannot be adequately modeled at a large geographical scale and a large time scale. For example, the production of heavy oil from UCS using the technique of Cold Heavy Oil Production with Sand (“CHOPS”) involves mechanisms that are not accurately modeled at large geographical and time scales.
More specifically, in this CHOPS recovery method, unconsolidated sand particles are produced along with the heavy oil being withdrawn from the formation. This sand removal results in structural voids in the sub-surface, such voids referred to in the art as “wormholes”. These wormholes tend to be generally cylindrical zones of high permeability originating from the wellbore perforations, with the high permeability of course expressing the effect of the withdrawing of sand from the formation. One conventional CHOPS simulation approach approximates wormholes as wellbores growing in the direction of the highest porosity (e.g., as indicated in a model of the sub-surface formations), at explicitly set growth rates. In essence, this approach computes a priori static wormhole trajectories that are independent of the sub-surface pressure gradient, in advance of executing the reservoir flow simulation. This simulation model has been shown to reasonably capture some empirical data patterns, including the effects of solution gas drive and aquifer support, as described in Vittoratos et al., “Deliberate Sand Production from Heavy Oil Reservoirs: Potent Activation of Both Solution Gas and Aquifer Drives”, Proceedings for the World Heavy Oil Congress 2008, Paper 2008-501, incorporated herein by this reference. But it has been recognized that this simulation of wormhole growth is necessarily inaccurate, based on anecdotal evidence that factors other than porosity are important in the growth and branching of such wormholes. In addition, it is contemplated according to this invention that the large modeled volumes and long time periods over which conventional reservoir simulation is applied, as compared with the small regions and short time frames at which the wormhole formation mechanism occurs, limits the ability of current-day simulation frameworks to predict and simulate wormhole formation.
As mentioned above, secondary recovery actions such as water injection are important for maximizing production from existing reservoirs, including moderately heavy oil-bearing UCS formations, especially in this economic climate. As known in the art, waterflood “fingers” are commonly formed from the injection wellbores, especially in relatively loose formations such as sands. These waterflood fingers amount to voids in the formation that become essentially filled with the injected water. While secondary waterflooding is not typically used in conjunction with the CHOPS recovery technique, wormholes can form unintentionally at producer wells in UCS formations. Sub-surface connection between such a wormhole and a waterflood finger can cause a “matrix bypass event” in which the injected water is short-circuited to a producing well, disrupting oil production at that well and preventing significant drive pressure from being applied to the reservoir. Improved accuracy in the simulation of wormhole formation in UCS formations would therefore be beneficial in assisting in the placement and management of injection wells in the UCS production field, and thus in the optimization of production from the field.
Other phenomena in the production of oil and gas also occur over short time frames and small volumes in the larger reservoir. For example, hydraulic fracturing of tight gas formations is important in maximizing production from tight gas formations; the mechanisms involved in creating the fractures both mechanically and chemically, and in injecting “proppants” of the appropriate size and composition to keep the fractures open, operate on relatively small relevant volumes and short time frames, and are thus poorly modeled by conventional simulation tools. Similarly, the physical mechanisms involved in oil sand perforators also operate over short time periods and small volumes.
By way of further background, a numerical technique referred to as “material point methods” (“MPM”) has been used in the simulation of the effects of weapons and ordnance. MPM modeling uses both a Eulerian mesh and Lagrangian points to represent a material. The Lagrangian integration points move through the Eulerian mesh during the simulation time period. In a general sense, these particles move independently relative to one another (and are not connected to one another, as are mesh nodes in the mesh), but are influenced by their near neighbors at each simulation time point, according to particular shape functions. In each simulation time step, equations of motion are solved at grid cells of the Eulerian mesh, and for the Lagrangian particles moving through that mesh. MPM methods have been applied to simulations of projectile-target interaction, including the interaction of an explosive projectile impacting a metal body and explosions near modeled buildings.